Defining acivation function, Should I write lambda x: numpy.tanh(x) OR only numpy.tanh?

Question

在定義激活 function (tanh) 時，是否需要編寫 lambda x: numpy.tanh(x)？ 或者我應該只寫激活 function = numpy.tanh？

這是我的代碼 class 神經網絡：

# initialise the neural network
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
    # set number of nodes in each input, hidden, output layer
    self.inodes = inputnodes
    self.hnodes = hiddennodes
    self.onodes = outputnodes
    
    # link weight matrices, wih and who
    # weights inside the arrays are w_i_j, where link is from node i to node j in the next layer
    # w11 w21
    # w12 w22 etc 
    self.wih = numpy.random.normal(0.0, pow(self.inodes, -0.5), (self.hnodes, self.inodes))
    self.who = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.onodes, self.hnodes))

    # learning rate
    self.lr = learningrate
    
    # activation function is the sigmoid function
    self.activation_function = numpy.tanh
    
    pass

Answer 1

不同之處在於

g = lambda x: f(x)

對比

g = f

創建一個額外的匿名 function，它通過調用 f. 因此，這引入了額外的計算成本，絕對沒有任何好處。